Implementation using Tools:
D3.js:
Description: The unprocessed dataset’s initial representation was done using D3.js (Data-Driven Documents). It provided a means
of creating real-time graphs and charts that could be directly included in web browsers.
Usage: D3.js helped us create interactive visualizations such as bar plots, scatter plots and heatmaps to explore the raw data
structure and patterns in it.
Python:
Description: Python with libraries like Pandas, NumPy, Matplotlib and Seaborn played a key role in preprocessing, analysis and
visualization of the data.
Usage: Missing values were imputed using Pandas and Numpy during data cleaning as well as removing any outliers and
consistencies. However, cleaned datasets were then used to plot refined visualizations through Matplotlib & Seaborn which provide
insights through multiple types of charts & plots.
Microsoft Power BI:
Description: Microsoft Power BI acted as an all-inclusive platform for building interactive dashboards and reports while
integrating the use of visualizations giving a holistic view of analyzed data.
Usage: Power BI allowed us to create interactive visualizations such as bar charts, line graphs or maps for instance and then
include them in interactive dashboards without any hindrances whatsoever. Besides data modeling capabilities in Power BI
facilitated relationship building.